Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects

doi:10.5498/wjp.119455

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World Journal of Psychiatry

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2220-3206

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Psychiatry. Sep 19, 2026; 16(9): 119455
Published online Sep 19, 2026. doi: 10.5498/wjp.119455

Table 5 Summary of model performance indicators for the external validation cohort

	Traditional clinical model	Integrated model
AUC (95%CI)	0.823 (0.692-0.954)	0.888 (0.764-1.000)
Sensitivity	0.200	0.400
Specificity	0.884	0.978
Accuracy	0.750	0.867
F1 score	0.235	0.533
DeLong’s test	-	P < 0.05

AUC: Area under the curve; CI: Confidence interval.

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Citation: Luan FF, Chen JY, Zheng YZ, Hu MH, Huang F, Zheng CX. Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects. World J Psychiatry 2026; 16(9): 119455
URL: https://www.wjgnet.com/2220-3206/full/v16/i9/119455.htm
DOI: https://dx.doi.org/10.5498/wjp.119455

Luan FF, Chen JY, Zheng YZ, Hu MH, Huang F, Zheng CX. Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects. World J Psychiatry 2026; 16(9): 119455 [DOI: 10.5498/wjp.119455]

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